measurement and characterization of finer details, such as qualitative changes in land use or cover (e.g., agricultural intensification or variations in net primary productivity) or modification in driver variables (e.g., changes in agricultural inputs such as fertilizers). The use of crisp categories simplifies analysis, but it requires that the number of categories to use must be determined and categories must be applied consistently over time. More categories add more detail, but they also make the analysis more complicated. It can be desirable to aggregate categories to reduce the data to a small number of important categories; the manner in which aggregation is performed influences the signals of land change.

Data accuracy and reliability

Understanding the accuracy and reliability of the data used in LCMs is essential. With remotely sensed data, accuracy is affected by the data source (i.e., sensor) and by the processing steps involved in creating the final map. The lineages of other LCM data are often more difficult to identify. For example, the specific instruments, field methods, original source material, or analyses used may not be recorded or evident. Consequently, it is often difficult to assess the accuracy or reliability of these other data sources. Additionally, the increasing availability of “volunteered geographic information” (Goodchild, 2007), for which individual citizens provide geographic information, presents a new trove of information for observing and analyzing change (NRC, 2010c). While the accuracy of these data is nearly impossible to assess, especially given the magnitude of the information available and the lack of strict protocols in its collection, the volume of data can often be used in creative ways to develop robust characterizations of the phenomenon of interest.

Multiple time points

Inherent to all LCMs is information over time. New methods are needed to consider the use of data with high temporal frequency in LCMs (de Beurs and Henebry, 2005). While the availability of temporal data from coarse-resolution imagery (i.e., > 100 m resolution) has been common for a couple decades, the availability of high-frequency data with finer resolutions (i.e., <50 m resolution) is a recent occurrence. A challenge for other data sources, such as field surveys and censuses, is how to sustain consistent collection efforts over the long term to create time-series data that could be more useful for LCMs.

An Abundance of New Data Sources

The growth of new data sources from satellites, aircraft, ground sensors, and “citizen science” presents new opportunities to measure and characterize land systems. The massive increase in data-gathering methods and data sets has not been matched by parallel increases in approaches for turning raw geographic data into more meaningful information about land systems or inputs to LCMs. The



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